1. Field
A video infrared retinal image scanner uses an infrared light to illuminate an ocular system and a camera to capture and display an image. The image may be analyzed and processed and rendered in 3-D. Computer analysis of the retinal vessels is performed by looking at the branching pattern of the retinal vessels. An overlay of the retinal vessels may be compared to previous scans to identify a person.
2. Description of the Related Art
The patterns of branching of the retinal vessels in an eye are unique and can be used as a form of identification. Biometric identification systems involve the use of finger prints, retinal blood vessel patterns, voice dynamics, hand geometry, facial recognition, and hand writing dynamics for identification of an individual.
These vessels can be recorded using a retinal scan of the retinal vessels. The retinal scan can be used to overlay the vessels pattern over a preexisting image to match the images. The retinal vessel branching pattern can also be analyzed by computer software to compare to the branching pattern of a previous image for identification. An accurate identification can be obtained by comparing this image to a database of retinal scans.
Biometric accuracy is measured in two ways, the rate of false acceptance (an impostor is accepted as a match—Type 1 error) and the rate of false rejects (a legitimate match is denied—Type 2 error). Every biometric technique has a different method of assigning a “score” to the biometric match; a “threshold value” is defined which determines when a match is declared. Scores above the threshold value are designated as a “Hit” and scores below the threshold are designated as “No-Hit.”
A Type 2 error occurs if a true match does not generate a score above the threshold. A Type 1 error is made when an impostor generates a match score above the threshold. If the Type 1 and Type 2 error rates are plotted as a function of threshold value, they will form curves which intersect at a given threshold value. The point of intersection (where Type 1 error equals Type 2 error) is called the crossover accuracy of the system. In general, as the value of the crossover accuracy increases the inherent accuracy of the biometric increases. The crossover accuracies of various means of identification are shown in Table I.
TABLE ICrossoverBiometricAccuracyRetinal Scan1:10,000,000+Retina Scan1:131,000Fingerprints1:500Hand Geometry1:500 (against a very smallbackground database)Signature Dynamics1:50Voice Dynamics1:50Facial Recognitionno dataVascular Patternsno data
Despite a high cross over accuracy, retinal scans are not without problems obtaining a good image.
There are sources of problems that could affect the performance of the retinal scanning device from obtaining an accurate scan (as is the case with any other biometric technology), thus impacting its ability to successfully verify or identify users. Among the problems are:
The lack of cooperation on part of the user. The user must remain very still in the entire process, especially in the image acquisition phase. Any movement can seriously affect the alignment of the lens in the retinal scanning device.
A dirty lens on the retinal scanning device. This will obviously interfere with the scanning process.
Other types of light interference from the external environment.
The pupil size of the user. A small pupil that is constricted to a further, smaller size because of a bright lighting environment can reduce the amount of light that reaches the retina via the pupil and vice versa. This can cause the system to have a higher rate of False Rejection.